AI Native Software Engineer

About the Role

Build and ship production-ready software using AI as a core part of the engineering lifecycle. Apply AI-native practices to develop and validate solutions, maintain quality and compliance, and continuously improve how software is built and operated.

Key Responsibilities

• Build and deploy production-grade AI applications using LLMs, RAG pipelines, and agentic systems.
• Work across backend (Python/FastAPI) and frontend (React/JavaScript) stacks.
• Deploy and operate AI services in a cloud-native Kubernetes environment.
• Leverage modern GenAI frameworks and tooling to accelerate development.
• Design and develop LLM-powered backend services using Python and FastAPI.
• Build Retrieval-Augmented Generation (RAG) pipelines using vector databases.
• Implement agentic workflows using frameworks such as AutoGen or similar.
• Design and expose RESTful APIs following OpenAPI / REST API specifications.
• Integrate backend AI services with React + JavaScript frontend applications.
• Deploy, manage, and scale AI services on Kubernetes.
• Use LangChain to orchestrate prompts, tools, chains, memory, and agents.
• Use LangSmith for:
• Prompt/version tracking
• LLM tracing and debugging
• Evaluation and performance monitoring
• Apply prompt engineering best practices and maintain reusable prompt libraries.
• Collaborate with cross-functional teams to deliver end-to-end AI solutions.
• Ensure security, reliability, observability, and performance of AI systems.

Requirements & Tech Stack

Backend & APIs

• Strong hands-on experience with:
• Python
• FastAPI
• Experience designing and consuming RESTful APIs.
• Familiarity with OpenAPI / Swagger specifications.

Frontend

• Working knowledge of:
• React
• JavaScript
• Experience integrating frontend applications with backend APIs.

Generative AI & LLMs

• Hands-on experience working with Large Language Models (LLMs).
• Agentic system design patterns knowledge.
• Experience building RAG-based applications.
• Knowledge of:
• Embeddings
• Vector search
• Context management
• Practical experience with prompt engineering.

GenAI/Agentic Frameworks & Tooling (Must Have)

• LangChain
• Chains, tools, agents, memory
• LangSmith
• Prompt tracing, debugging, evaluation, and monitoring
• Agentic frameworks
• AutoGen or similar
• Experience with multi-agent orchestration, tool-calling agents, planners, and executors.
• Protocols like MCP / A2A.

Cloud & DevOps

• Experience with:
• Docker
• Kubernetes
• Understanding of cloud setup and deployment (AWS / Azure / GCP).
• Basic CI/CD knowledge is a plus.

Gen AI Tools

• AI productivity tools for general development:
• GitHub Copilot
• Claude / Cursor / Cline or similar.

Good to Have/Differentiators

• Working knowledge/experience with Vector databases: Pinecone, FAISS, Weaviate, Chroma, or equivalent.
• Graph databases: Apache AGE, Neo4j etc.
• Strong basics in Security/AuthN/AuthZ: JWT, OAuth2.
• Working knowledge of monitoring and observability tools.

Soft Skills

• Strong problem-solving and debugging skills.
• Ability to work independently and take ownership.
• Clear communication and collaboration skills.
• High curiosity for fast-evolving AI and GenAI ecosystems.

Qualifications

• 15 years full-time education.